Volume visualization has become increasingly important in clinical practice for the display and analysis of volumetric diatasets acquired with imaging methods such as Computed Tomography (CT) or Magnetic Resonance Enaging (MRI). Benefits of volume visualization include the ability to obtain oblique views, the increased understanding of complex geometric structures; and the ability to measure volumes, areas, and distances. Volume visualization also provides the ability to explore the spatial relationship between an organ and its surrounding structures or tissues. There are two main classes of volume visualization techniques; surface rendering and volume rendering. The present invention relates to volume rendering which is a technique that generates a two-dimensional (2-D) projection directly from the 3-D volume data without requiring any intermediate geometrical data structure.
Many different approaches to volume rendering have been proposed over the years. However, they all suffer to some extent from the problem that volume rendering algorithms are computationally and memory intensive, although, ever-increasing processor speed, affordable memory, and clever optimizations have made it possible to perform volume rendering on low-cost, general purpose personal computers. However, even with interactive rendering speed, the exploration and visualization of a volumetric dataset is a challenging task. The increasing amount of data produced by the imaging devices and the occlusion of the structures of interest by other portions of the subject call for flexible methods to focus the visualization to a very specific part of the dataset.
The classical approach to address these issues has been 3-D anatomical feature extraction by segmentation. These methods tend to be very time consuming (i.e. slice by slice tracing) or very anatomy and/or imaging modality specific. A number of researchers have proposed a more general semi-automatic segmentation method, however the binary decision process imposed by the segmentation method often results in loss of information and reduced image quality. See, for example, Schiemann et al., “Interactive 3D Segmentation”, Visualization in Biomedical Computing 1992, SPIE Vol. 1808, pp. 376–383, which is incorporated herein by reference.
An alternative approach is to remove regions with no diagnostic value or regions that obscure the structure of interest, rather than extracting the structure itself. The classical axis-aligned cut plane model is often used for this purpose but in many cases, when complicated morphologies are present, the results are far from optimal. Pflessor et al., “Towards Realistic Visualization for Surgery Rehearsal” First International Conference of Compute Vision, Virtual Reality and Robotics in Medicine (CVR Med '95), pp. 487–491, April 1995, which is incorporated herein by reference, presents a volume sculpting technique with application to surgery rehearsal. However, in this technique a segmentation step is required prior to data manipulation. The technique introduced sculpting tools that perform free-form cutting of pre-segmented objects. Wang and Kaufrnan “Volume Sculpting”, 1995 Symposium on Interactive 3D Graphics, pp. 157–156, April 1995, which is incorporated herein by reference, provides a similar technique but with the focus on a modeling tool for the creation of a three-dimensional object, not on the extraction of specific features from a volume dataset.
It is an object of the present invention to provide a system and method of volume rendering which overcomes the deficiencies of the prior art.